Simulación y visualización de datos espacio-temporales en brotes de infecciones hospitalarias

Abstract

Multidrug-resistant (MDR) bacteria are microorganisms that have developed resistance to the antimicrobials commonly used against them. They are mainly associated with nosocomial infections and represent a serious threat to public health due to their rapid spread and associated clinical complications. The World Health Organization highlights the need to develop better control and prevention tools, and strengthen health systems for its detection and control in hospital settings. This control is carried out through the study of clinical data, which allows obtaining epidemiological information, identifying patterns and trends, and analyzing diseases spatially and temporally. However, this type of data is usually not open, because it contains sensitive information about individuals. In the few cases they are available, they constitute a small group of patients, focused on the specific study of a clinical problem. As a consequence, these data cannot be reused for multiple purposes and often have bias problems. This thesis is based on the hypothesis that the representation of the spatial and temporal dimensions of epidemiological data facilitates understanding and helps decision-making in hospitals in the face of epidemic situations caused by MDR bacteria. To test this hypothesis, this thesis focuses on 2 fields: On the one hand, we will work on simulation models with the following objectives: design and develop a spatio-temporal simulation model of an epidemiological problem due to MDR bacteria in hospitals, as well as the behavior of patients and the operation of a hospital. Following this, generate synthetic spatio-temporal data sets of nosocomial infections by MDR bacteria. On the other hand, we will work on visualization techniques with the following objectives: study spatial and temporal visualization techniques and methods to explain the results of epidemiological analyses. In addition to this, study the use of software tools, open access data sets, and evaluation methods in the implementation of said visualization techniques. Subsequently, design and propose visual techniques for decision-making with spatio-temporal information of an epidemic situation in a hospital environment. Finally, develop and evaluate a visual interactive tool for the study of hospitalized patients and the spread of an MDR bacteria infection. The conclusions of this doctoral thesis regarding the objectives are: (1) we identified 4 uses of spatial-temporal visualization techniques: data presentation, real-time data detection, post-analysis, and trend prediction. The most used visualization techniques capture the situation of a population, compared to few studies at the individual level and representations of buildings. (2) A predominance of simple geographic and web programs is observed, but best practices are required for effective visualizations. There is more and more public access to data, but they often contain aggregated information. (3) The combination of micromodels and macromodels proves to be effective in creating a simulation model to study MDR bacterial infections in hospitals on individual patients with epidemiological utility. (4) The use of movement rules and spatial-temporal limitations of patients in the simulation model allows the analysis of virtual scenarios faithful to reality. (5) The proposed simulation models allow the generation of realistic and open access data for use in the scientific community. (6) The use of standardized methodologies and questionnaires are effective in modeling views and interactions to obtain an optimal tool and more efficient communication between participants. (7) The interactivity and dynamism of the visual tool allows faithful representation of the movements and processes of infection. The use of generalized graphs helps to understand temporal progress using epidemiological indicators. The combination of dynamic and static visualization techniques offers different perspectives on the data, making it easier to understand and recognize new insights. The evaluation with 14 professionals has yielded positive results about the potential use of this tool in their workflow